Introduction

Project Overview

Group number: 11 Group memebers: Robbe Claeys & Simon Bil

Research question: An omics-wide analysis of Vitamin D difieciency.

Data Overview

Paper links:

ChIP seq

ChIP & RNA seq

RNA microarray

Download links: RNA microarray

RNA seq

ChIP seq

table_general_info <- rbind(c("E-GEOD-22523", "GSE69284", "GSE89431", "GSE89431"), c(8, 18, 39, 2), c(8, 2, 6, 2))

colnames(table_general_info) <- c("Microarray Data", "RNAseq Data", "ChIPseq Data", "ChIPseq Data")

rownames(table_general_info) <- c("Accesion ID", "Number of Samples (original)", "Number of Samples (analysed)")

table_general_info
##                              Microarray Data RNAseq Data ChIPseq Data
## Accesion ID                  "E-GEOD-22523"  "GSE69284"  "GSE89431"  
## Number of Samples (original) "8"             "18"        "39"        
## Number of Samples (analysed) "8"             "2"         "6"         
##                              ChIPseq Data
## Accesion ID                  "GSE89431"  
## Number of Samples (original) "2"         
## Number of Samples (analysed) "2"

Microarray Data

General info

This dataset (E-GEOD-22523) was generated in 2010 and aimed to identify genes regulated by Vit D in B cells. A gene expression microarray analysis in HVDRR EBV-transformed B-cells and control cells was performed in the presence or absence of vitamin D. This study was performed on 8 samples (4 cases & 4 controls), all were used for our data analysis.

Microarray type: Affymetrix Human Gene 1.0 ST Array [HuGene-1_0-st]

Intensity values

Load libraries

library(affy)
library(oligo)
library(arrayQualityMetrics)
library(ArrayExpress)
library(limma)
library(siggenes)

Load the data

id <- "E-GEOD-22523"
exonCELs <- list.celfiles("./data/microarray/")
arrayRaw <- read.celfiles(paste(rep("./data/microarray/", length(exonCELs)), exonCELs, sep = ""))

The first 4 .CEL files contain the data for the hereditary vitamin D-resistant rickets (HVDRR) cells, of which the first two are biological replicates and the last two were treated with Vit D. The last 4 .CEL files contain the data for the Wild-type (WT) cell, again, of which the first two are biological replicates and the last two were treated with Vit D.

Get dimensions and general overview of the data.

dim(exprs(arrayRaw))
## [1] 1102500       8
head(exprs(arrayRaw))
##   GSM559273.CEL GSM559274.CEL GSM559275.CEL GSM559276.CEL GSM559277.CEL
## 1         12499         10219         11061         10341          9130
## 2           231           201           258           241           184
## 3         12499         10302         11354         11102          9058
## 4            96           114           125           151            97
## 5            53            51            60            69            53
## 6           174           124           160           218           163
##   GSM559278.CEL GSM559279.CEL GSM559280.CEL
## 1          9049         11657          7674
## 2           140           271           142
## 3          9215         12246          6796
## 4            74           101            85
## 5            52            58            41
## 6            91           195           116

Annotation

We will be investigating differential expression between . No other confounding factors are present in the sample annotation (so far).

sdrf <- read.delim("./data/microarray/E-GEOD-22523.sdrf.txt")
print(sdrf[, c("Source.Name", "Comment..Sample_source_name.", "Array.Design.REF")])
##   Source.Name      Comment..Sample_source_name. Array.Design.REF
## 1 GSM559280 1 Wild type EBV-transformed B-cells       A-AFFY-141
## 2 GSM559279 1 Wild type EBV-transformed B-cells       A-AFFY-141
## 3 GSM559278 1 Wild type EBV-transformed B-cells       A-AFFY-141
## 4 GSM559277 1 Wild type EBV-transformed B-cells       A-AFFY-141
## 5 GSM559276 1     HVDRR EBV-transformed B-cells       A-AFFY-141
## 6 GSM559275 1     HVDRR EBV-transformed B-cells       A-AFFY-141
## 7 GSM559274 1     HVDRR EBV-transformed B-cells       A-AFFY-141
## 8 GSM559273 1     HVDRR EBV-transformed B-cells       A-AFFY-141

ChIPseq Data - GSE89431

General info

This dataset (GSE89431) was generated in 2017 and aimed to identify genomic locations bound by the vitamin D receptor (VDR). ChIP-seq for the vitamin D receptor (VDR) was performed in THP-1 cells after stimulation with the natural VDR ligand 1,25-dihydroxyvitamin D3 (1,25(OH)2D3) or with vehicle for 2 or 24h before ChIP assay.

  • Read length: 51bp
  • Single/paired end sequencing: single end sequencing
  • Platform used: Illumina HiSeq 2000 (Homo sapiens)

Quality control on fastQ files

MultiQC Report

Highlight Samples

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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.9

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2022-11-14, 21:36 based on data in: /kyukon/data/gent/vo/000/gvo00095/Courses/AHTA/Group11/GSE89431


        General Statistics

        Showing 12/12 rows and 3/5 columns.
        Sample Name% Dups% GCM Seqs
        SRR4828890
        7.5%
        41%
        10.1
        SRR4828891
        4.4%
        41%
        10.6
        SRR4828892
        7.5%
        42%
        11.3
        SRR4828893
        6.4%
        41%
        10.3
        SRR4828894
        8.1%
        43%
        9.9
        SRR4828895
        8.4%
        43%
        8.0
        SRR4828896
        11.6%
        42%
        38.5
        SRR4828897
        13.0%
        43%
        57.8
        SRR4828898
        14.4%
        43%
        9.7
        SRR4828899
        8.0%
        42%
        9.4
        SRR4828900
        8.2%
        42%
        11.3
        SRR4828901
        7.4%
        43%
        9.9

        FastQC

        FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        loading..

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        loading..

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        loading..

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        loading..

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        loading..

        Sequence Length Distribution

        All samples have sequences of a single length (51bp).

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        loading..

        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        12 samples had less than 1% of reads made up of overrepresented sequences

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        No samples found with any adapter contamination > 0.1%

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        loading..

        Mapping statistics

        We tried to perform mapping ourselves, but failed at a certain point. For this reason we did not have time to finish peak data and annotation on the ChIPseq data.

        Annotation

        gse89431_annot <- read.csv("./data/annotation/GSE89431/SraRunTable.txt")
        print(gse89431_annot[c("Run", "Treatment", "Time")])
        ##           Run            Treatment Time
        ## 1  SRR4828890                 EtOH  2 h
        ## 2  SRR4828891 100 nM 1\\,25(OH)2D3  2 h
        ## 3  SRR4828892                 EtOH  2 h
        ## 4  SRR4828893 100 nM 1\\,25(OH)2D3  2 h
        ## 5  SRR4828894                 EtOH  2 h
        ## 6  SRR4828895 100 nM 1\\,25(OH)2D3  2 h
        ## 7  SRR4828896                 EtOH 24 h
        ## 8  SRR4828897 100 nM 1\\,25(OH)2D3 24 h
        ## 9  SRR4828898                 EtOH 24 h
        ## 10 SRR4828899 100 nM 1\\,25(OH)2D3 24 h
        ## 11 SRR4828900                 EtOH 24 h
        ## 12 SRR4828901 100 nM 1\\,25(OH)2D3 24 h

        ChIPseq Data - GSE22484

        General info

        This dataset (GSE22484) was generated in 2010 and aimed to identify the genomic locations bound by the vitamin D receptor (VDR) in two lymphoblastoid cell lines before and after calcitriol (active form of vitamin D) treatment for 36 hours.

        • Read length: 36bp
        • Single/paired end sequencing: single end sequencing
        • Platform used: Illumina Genome Analyzer II (Homo sapiens)

        Quality control

        MultiQC Report

        Highlight Samples

        Regex mode off

          Rename Samples

          Click here for bulk input.

          Paste two columns of a tab-delimited table here (eg. from Excel).

          First column should be the old name, second column the new name.

          Regex mode off

            Show / Hide Samples

            Regex mode off

              Export Plots

              px
              px
              X

              Download the raw data used to create the plots in this report below:

              Note that additional data was saved in multiqc_data when this report was generated.


              Choose Plots

              If you use plots from MultiQC in a publication or presentation, please cite:

              MultiQC: Summarize analysis results for multiple tools and samples in a single report
              Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
              Bioinformatics (2016)
              doi: 10.1093/bioinformatics/btw354
              PMID: 27312411

              Save Settings

              You can save the toolbox settings for this report to the browser.


              Load Settings

              Choose a saved report profile from the dropdown box below:

              About MultiQC

              This report was generated using MultiQC, version 1.9

              You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

              For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

              You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

              MultiQC is published in Bioinformatics:

              MultiQC: Summarize analysis results for multiple tools and samples in a single report
              Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
              Bioinformatics (2016)
              doi: 10.1093/bioinformatics/btw354
              PMID: 27312411

              A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

              Report generated on 2022-11-14, 18:17 based on data in: /kyukon/data/gent/vo/000/gvo00095/Courses/AHTA/Group11


              General Statistics

              Showing 10/10 rows and 3/5 columns.
              Sample Name% Dups% GCM Seqs
              SRR058460
              19.4%
              42%
              18.3
              SRR058461
              22.3%
              45%
              15.4
              SRR058462
              17.1%
              42%
              18.4
              SRR058463
              16.3%
              41%
              19.0
              SRR058464
              7.9%
              39%
              12.3
              SRR058465
              7.1%
              45%
              10.1
              SRR058466
              8.4%
              44%
              13.3
              SRR058467
              8.0%
              43%
              14.5
              SRR058468
              10.4%
              41%
              15.0
              SRR058469
              6.2%
              40%
              13.7

              FastQC

              FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

              Sequence Counts

              Sequence counts for each sample. Duplicate read counts are an estimate only.

              This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

              You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

              Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

              The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

              loading..

              Sequence Quality Histograms

              The mean quality value across each base position in the read.

              To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

              Taken from the FastQC help:

              The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

              loading..

              Per Sequence Quality Scores

              The number of reads with average quality scores. Shows if a subset of reads has poor quality.

              From the FastQC help:

              The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

              loading..

              Per Base Sequence Content

              The proportion of each base position for which each of the four normal DNA bases has been called.

              To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

              To see the data as a line plot, as in the original FastQC graph, click on a sample track.

              From the FastQC help:

              Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

              In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

              It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

              Click a sample row to see a line plot for that dataset.
              Rollover for sample name
              Position: -
              %T: -
              %C: -
              %A: -
              %G: -

              Per Sequence GC Content

              The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

              From the FastQC help:

              This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

              In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

              An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

              loading..

              Per Base N Content

              The percentage of base calls at each position for which an N was called.

              From the FastQC help:

              If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

              It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

              loading..

              Sequence Length Distribution

              All samples have sequences of a single length (36bp).

              Sequence Duplication Levels

              The relative level of duplication found for every sequence.

              From the FastQC Help:

              In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

              Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

              The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

              In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

              loading..

              Overrepresented sequences

              The total amount of overrepresented sequences found in each library.

              FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

              Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

              From the FastQC Help:

              A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

              FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

              10 samples had less than 1% of reads made up of overrepresented sequences

              Adapter Content

              The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

              Note that only samples with ≥ 0.1% adapter contamination are shown.

              There may be several lines per sample, as one is shown for each adapter detected in the file.

              From the FastQC Help:

              The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

              No samples found with any adapter contamination > 0.1%

              Status Checks

              Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

              FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

              It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

              Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

              In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

              loading..

              Mapping statistics

              We tried to perform mapping ourselves, but failed at a certain point. For this reason we did not have time to finish peak data and annotation on the ChIPseq data.

              Annotation

              gse22484_annot <- read.csv("./data/annotation/GSE22484/SraRunTable.txt")
              print(gse22484_annot[c("Run", "Treatment", "SAMPLE_TYPE")])
              ##          Run    Treatment                                        SAMPLE_TYPE
              ## 1  SRR058460 unstimulated                                                   
              ## 2  SRR058461 unstimulated                                                   
              ## 3  SRR058462   calcitriol                                                   
              ## 4  SRR058463   calcitriol                                                   
              ## 5  SRR058464 unstimulated                                                   
              ## 6  SRR058465 unstimulated                                                   
              ## 7  SRR058466   calcitriol                                                   
              ## 8  SRR058467   calcitriol                                                   
              ## 9  SRR058468              mixture of equal amounts of DNA from samples 01-04
              ## 10 SRR058469              mixture of equal amounts of DNA from samples 05-08

              RNAseq Data - GSE69284

              General info

              This dataset (GSE69284) was generated in 2016 and aimed to identify genes regulated by Vit D in the human monocytic THP-1 cell line. Expression profiling was performed through high throughput RNA sequencing after treatment with Vit D3 for 2.5-24 h. This study was performed on 18 samples (in trireplicates), 2 were used for our data analysis (1 case and 1 control).

              • Read length: 51bp
              • Single/paired end sequencing: (single end sequencing)
              • Platform used: Illumina Genome Analyzer II

              Quality control

              MultiQC Report

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                    If you use plots from MultiQC in a publication or presentation, please cite:

                    MultiQC: Summarize analysis results for multiple tools and samples in a single report
                    Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
                    Bioinformatics (2016)
                    doi: 10.1093/bioinformatics/btw354
                    PMID: 27312411

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                    About MultiQC

                    This report was generated using MultiQC, version 1.13

                    You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

                    For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

                    You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

                    MultiQC is published in Bioinformatics:

                    MultiQC: Summarize analysis results for multiple tools and samples in a single report
                    Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
                    Bioinformatics (2016)
                    doi: 10.1093/bioinformatics/btw354
                    PMID: 27312411

                    A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

                    Report generated on 2022-11-14, 18:12 CET based on data in: /home/robbe/Desktop/ghSchool/VitD-OmicsAnalysis/data/RNAseq


                    General Statistics

                    Showing 4/4 rows and 3/5 columns.
                    Sample Name% Dups% GCM Seqs
                    SRR2042588
                    48.1%
                    50%
                    29.8
                    SRR2042589
                    49.1%
                    50%
                    34.2
                    SRR2042594
                    48.6%
                    49%
                    31.5
                    SRR2042595
                    50.4%
                    49%
                    34.2

                    FastQC

                    FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

                    Sequence Counts

                    Sequence counts for each sample. Duplicate read counts are an estimate only.

                    This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

                    You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

                    Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

                    The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

                    loading..

                    Sequence Quality Histograms

                    The mean quality value across each base position in the read.

                    To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

                    Taken from the FastQC help:

                    The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

                    loading..

                    Per Sequence Quality Scores

                    The number of reads with average quality scores. Shows if a subset of reads has poor quality.

                    From the FastQC help:

                    The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

                    loading..

                    Per Base Sequence Content

                    The proportion of each base position for which each of the four normal DNA bases has been called.

                    To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

                    To see the data as a line plot, as in the original FastQC graph, click on a sample track.

                    From the FastQC help:

                    Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

                    In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

                    It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

                    Click a sample row to see a line plot for that dataset.
                    Rollover for sample name
                    Position: -
                    %T: -
                    %C: -
                    %A: -
                    %G: -

                    Per Sequence GC Content

                    The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

                    From the FastQC help:

                    This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

                    In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

                    An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

                    loading..

                    Per Base N Content

                    The percentage of base calls at each position for which an N was called.

                    From the FastQC help:

                    If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

                    It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

                    loading..

                    Sequence Length Distribution

                    All samples have sequences of a single length (51bp).

                    Sequence Duplication Levels

                    The relative level of duplication found for every sequence.

                    From the FastQC Help:

                    In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

                    Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

                    The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

                    In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

                    loading..

                    Overrepresented sequences

                    The total amount of overrepresented sequences found in each library.

                    FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

                    Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

                    From the FastQC Help:

                    A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

                    FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

                    4 samples had less than 1% of reads made up of overrepresented sequences

                    Adapter Content

                    The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

                    Note that only samples with ≥ 0.1% adapter contamination are shown.

                    There may be several lines per sample, as one is shown for each adapter detected in the file.

                    From the FastQC Help:

                    The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

                    No samples found with any adapter contamination > 0.1%

                    Status Checks

                    Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

                    FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

                    It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

                    Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

                    In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

                    loading..

                    Annotation

                    gse69284_annot <- read.csv("./data/annotation/GSE69284/SraRunTable.txt")
                    print(gse69284_annot[c("Run", "Treatment", "treatment_time")])
                    ##           Run            Treatment treatment_time
                    ## 1  SRR2042580              Ethanol          2.5 h
                    ## 2  SRR2042581 100 nM 1\\,25(OH)2D3          2.5 h
                    ## 3  SRR2042582              Ethanol            4 h
                    ## 4  SRR2042583 100 nM 1\\,25(OH)2D3            4 h
                    ## 5  SRR2042584              Ethanol           24 h
                    ## 6  SRR2042585 100 nM 1\\,25(OH)2D3           24 h
                    ## 7  SRR2042586              Ethanol          2.5 h
                    ## 8  SRR2042587 100 nM 1\\,25(OH)2D3          2.5 h
                    ## 9  SRR2042588              Ethanol            4 h
                    ## 10 SRR2042589 100 nM 1\\,25(OH)2D3            4 h
                    ## 11 SRR2042590              Ethanol           24 h
                    ## 12 SRR2042591 100 nM 1\\,25(OH)2D3           24 h
                    ## 13 SRR2042592              Ethanol          2.5 h
                    ## 14 SRR2042593 100 nM 1\\,25(OH)2D3          2.5 h
                    ## 15 SRR2042594              Ethanol            4 h
                    ## 16 SRR2042595 100 nM 1\\,25(OH)2D3            4 h
                    ## 17 SRR2042596              Ethanol           24 h
                    ## 18 SRR2042597 100 nM 1\\,25(OH)2D3           24 h